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© 2013

Machine Learning in Medicine

Part Two

Textbook

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 1-7
  3. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 9-15
  4. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 17-26
  5. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 27-38
  6. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 39-44
  7. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 45-52
  8. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 53-64
  9. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 65-75
  10. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 77-91
  11. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 93-103
  12. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 105-113
  13. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 115-128
  14. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 129-137
  15. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 139-153
  16. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 155-161
  17. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 163-170
  18. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 171-185
  19. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 187-194
  20. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 195-206

About this book

Introduction

Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects.

Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.

Keywords

Bayesian networks Discrete wavelet analysis Protein and DNA sequence mining Support vector machines Various clustering models

Authors and affiliations

  1. 1.SliedrechtNetherlands
  2. 2., Dept. Epidemiology and BiostatisticsAcademic Medical CenterAmsterdamNetherlands

Bibliographic information

Industry Sectors
Biomedicine
Pharma
Health & Hospitals
Biotechnology
Consumer Packaged Goods

Reviews

From the reviews:

“This is the second volume of a novel publication on machine learning in medicine that details statistical analysis of complex data with many variables. … The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students as well as master’s and doctoral students in biostatistics and epidemiology. … The simple language and well-organized chapters are unsurpassed attributes of this book. It is an exceptional resource for a quick review of machine learning in medicine.” (Goral Panchal, Doody’s Book Reviews, October, 2013)